In many geodetic applications, methods and systems for position determination of a geodetic instrument are used, which are based on global positioning systems, such as, for example, GPS, GLONASS or the European Galileo system. These GNSS systems are based on the reception of satellite signals.
The requirements of vehicle guidance or navigation, e.g. in agricultural or mining applications in particular, have subtle differences to that of surveying, including a much stronger requirement for continuously available positioning.
A typical approach is to combine GNSS-sensors with sensor data, e.g. from magnetometers, acceleration sensors or angular rate sensors, in order to derive further information of the vehicle's position, attitude or the variation of these parameters. However, most sensors produce biased data with a dependency on several factors, e.g. time or temperature. Therefore, the application of static bias values leads to a limited description of sensor performance, especially with respect to long term variations.
The most problematic movement to determine is the roll, i.e. a rotational movement about the x-axis of the vehicle. Roll is routinely determined through existing techniques using two high performance GPS receivers. However, these solutions have some disadvantages including the following:
too expensive or complex for many applications;
suffer from too much latency; and
are vulnerable to all the issues affecting GNSS-only systems including insufficient satellite availability, poor Geometric Dilution of Precision (GDOP) and interruptions to base communications.
Low latency roll is also routinely determined through existing techniques using three orthogonally mounted accelerometers, where some degree of accuracy is achieved through factory calibration of the sensors for temperature changes. However, these solutions also have some drawbacks including the following:
too expensive or complex for many applications, because calibration and sensor selection are further steps added to the process;
not sufficiently accurate due to an imperfectly repeatable relationship between temperature and sensor bias;
do not address inevitable sensor bias drift over time due to crystal aging; and
without a rapid and reliable means for detecting a sensor that performs significantly out of specification early in its life as can occur with Micro-Electro-Mechanical System (MEMS) sensors.